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Computational and mathematical organization theory: Perspective and directions

  • Kathleen M. Carley
Article

Abstract

Computational and mathematical organization theory is an interdisciplinary scientific area whose research members focus on developing and testing organizational theory using formal models. The community shares a theoretical view of organizations as collections of processes and intelligent adaptive agents that are task oriented, socially situated, technologically bound, and continuously changing. Behavior within the organization is seen to affect and be affected by the organization's, position in the external environment. The community also shares a methodological orientation toward the use of formal models for developing and testing theory. These models are both computational (e.g., simulation, emulation, expert systems, computer-assisted numerical analysis) and mathematical (e.g., formal logic, matrix algebra, network analysis, discrete and continuous equations). Much of the research in this area falls into four areas: organizational design, organizational learning, organizations and information technology, and organizational evolution and change. Historically, much of the work in this area has been focused on the issue how should organizations be designed. The work in this subarea is cumulative and tied to other subfields within organization theory more generally. The second most developed area is organizational learning. This research, however, is more tied to the work in psychology, cognitive science, and artificial intelligence than to general organization theory. Currently there is increased activity in the subareas of organizations and information technology and organizational evolution and change. Advances in these areas may be made possible by combining network analysis techniques with an information processing approach to organizations. Formal approaches are particularly valuable to all of these areas given the complex adaptive nature of the organizational agents and the complex dynamic nature of the environment faced by these agents and the organizations.

Keywords

Network Analysis Formal Model Organizational Evolution Organizational Learning Organizational Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Alluisi, E.A. (1991), “The Development of Technology for Collective Training: SIMNET, a Case History”,Human Factors, 33(3), 343–362.Google Scholar
  2. Anderson, P.A. and G.W. Fischer (1986), “A Monte Carlo Model of a Garbage Can Decision Process”, in J.G. March and R. Weissinger-Baylon (Eds.)Ambiguity and Command: Organizational Perspectives on Military Decision Making, Marshfield, MA: Pitman.Google Scholar
  3. Arrow, K.J. and R. Radner (1979), “Allocation of Resources in Large Teams”.Econometrica, 47, 361–385.Google Scholar
  4. Arthur, W.B. (1991), “Designing Economic Agents That Act Like Human Agents: A Behavioral Approach to Bounded Rationality”,American Economic Review Papers and Proceedings, 81, 353–359.Google Scholar
  5. Baligh, H.H., R.M. Burton and B. Obel (1994), “Validating the Organizational Consultant on the Fly”, in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  6. Baligh, H.H., R.M. Burton, and B. Obel (1987), “Design of Organizational Structures: An Expert System Method,” in J.L. Roos (Ed.),Economics and Artificial Intelligence, Oxford, UK: Pergamon.Google Scholar
  7. Baligh, H.H., R.M. Burton and B. Obel (1990), “Devising Expert Systems in Organization Theory: The Organizational Consultant,” in M. Mausch (Ed.)Organization, Management, and Expert Systems, Berlin: Walter De Gruyter.Google Scholar
  8. Beroggi, G.E. and W.A. Wallace (1994), “A Decision Logic for Operational Risk Management,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  9. Bond, A. and L. Gasser (1988),Readings in Distributed Artificial Intelligence. San Mateo, CA: Kaufmann.Google Scholar
  10. Bonini, C.P. (1963),Simulation of Information and Decision Systems in the Firm, Englewood Cliffs: Prentice-Hall.Google Scholar
  11. Burt, R. S. (1973), “The Differential Impact of Social Integration on Participation in the Diffusion of Innovations,”Social Science Research, 2, 125–144.Google Scholar
  12. Burt, R.S. (1980), “Innovation as a Structural Interest: Rethinking the Impact of Network Position Innovation Adoption,”Social Networks, 4, 337–355.Google Scholar
  13. Burt, R.S. (1992).Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press.Google Scholar
  14. Burton, R.M. and B. Obel (1980), “A Computer Simulation Test of the M-form Hypothesis,”Administrative Science Quarterly, 25, 457–66.Google Scholar
  15. Burton, R.M. and B. Obel (1984).Designing Efficient Organizations: Modeling and Experimentation. Amsterdam: Elsevier Science.Google Scholar
  16. Cammarata, S., D. McArthur and R. Steeb (1983), “Strategies of Cooperation in Distributed Problem Solving.”Proceedings of the Eighth International Conference on Artificial Intelligence.Google Scholar
  17. Carley, K. (1986), “Efficiency in a Garbage Can: Implications for Crisis Management,” in J.G. March and R. Weissinger-Baylon (Eds.)Ambiguity and Command: Organizational Perspectives on Military Decision Making, Marshfield, MA: Pitman.Google Scholar
  18. Carley, K. (1990), “Coordinating for Success: Trading Information Redundancy for Task Simplicity.”Proceedings of the 23rd Annual Hawaii International Conference on System Sciences.Google Scholar
  19. Carley, K. (1991), “Designing Organizational Structures to Cope with Communication Breakdowns: A Simulation Model,”Industrial, Crisis Quarterly, 5, 19–57.Google Scholar
  20. Carley, K. (1991), “A Theory of Group Stability,”American Sociological Review, 56(3), 331–354.Google Scholar
  21. Carley, K. (1992), “Organizational Learning and Personnel Turnover,”Organization Science, 3(1), 20–46.Google Scholar
  22. Carley, K. (forthcoming), “Communication Technologies and Their Effect on Cultural Homogeneity, Consensus, and the Diffusion of New Ideas,”Sociological Perspectives.Google Scholar
  23. Carley, K., J. Kjaer-Hansen, M. Prietula, and A. Newell (1992), “Plural-Soar: A Prolegomenon to Artificial Agents and Organizational Behavior.” in Masuch M. and Warglien M. (Eds.),Artificial Intelligence in Organization and Management Theory. Amsterdam, The Netherlands: Elsevier Science Publishers.Google Scholar
  24. Carley, K. and Z. Lin Z. (1995), “Organizational Designs Suited to High Performance Under Stress,”IEEE—Systems, Man and Cybernetics, 25(1).Google Scholar
  25. Carley, K. and M. Prietula (1992). “Toward a Cognitively Motivated Theory of Organizations.”Proceedings of the 1992 Coordination Theory and Collaboration Technology Workshops: Symposium Conducted for the National Science Foundation.Google Scholar
  26. Carley, K. and M. Prietula (1994), “ACTS Theory: Extending the Model of Bounded Rationality,” in K. Carley and M. Prietula (Eds.),Computational Organization Theory. Hillsdale, NJ: Lawrence Erlbaum Assoc.Google Scholar
  27. Cohen, G.P. (1992), “The Virtual Design Team: An Information Processing Model of Coordination in Project Design Teams,” Ph.D. Dissertation, Stanford University, Department of Civil Engineering.Google Scholar
  28. Cohen, M.D. (1986), “Artificial Intelligence and the Dynamic Performance of Organizational Designs,” on J.G. March and R. Weissinger-Baylon, (Eds.),Ambiguity and Command: Organizational Perspectives on Military Decision Making. Marshfield, MA: Pitman.Google Scholar
  29. Cohen, M.D., J.B. March J.B. and J.P. Olsen (1972), “A Garbage Can Model of Organizational Choice,”Administrative Science Quarterly, 17(1), 1–25.Google Scholar
  30. Cohen, K.J. and R.M. Cyert (1965), “Simulation of Organizational Behavior,” in J.G. March (Ed.),Handbook of Organizations, Chicago, IL: Rand McNally.Google Scholar
  31. Corkill, D. (1979). “Hierarchical Planning in a Distributed Environment.”Proceedings of the Sixth International Joint Conference on Artificial Intelligence. Tokyo, Japan.Google Scholar
  32. Crecine, J.P. (1969).A Computer Simulation of Municipal Budgeting, Chicago, IL.: Rand McNally.Google Scholar
  33. Crowston, K. (1994). “Evolving Novel Organizational Forms,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  34. Cyert, R.M. and J.G. March (1963).A Behavioral Theory of the Firm. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  35. Davis, R. and R.G. Smith (1983), “Negotiation as a Metaphor for Distributed Problem Solving,”Artificial Intelligence, 20, 63–109.Google Scholar
  36. DeGroot, M.H. (1970).Optimal Statistical Decisions. New York, NY: McGraw-Hill.Google Scholar
  37. DeGroot, M.H. (1974), “Reaching a Consensus,”Journal of American Statistical Association 69, 118–121.Google Scholar
  38. Decker, K.S. and V.R. Lesser (1992), “Generalizing the Partial Global Planning Algorithm,”International Journal of Intelligent and Cooperative Information Systems, 1(2), 319–346.Google Scholar
  39. Decker, K.S. and V.R. Lesser (1993), “Analyzing a Quantitative Coordination Relationship,”Group Decision and Negotiation, 2(3), 195–217.Google Scholar
  40. DiMaggio, P.J. and W.W. Powell (1983), “The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields,”American Sociological Review, 147–160.Google Scholar
  41. DiMaggio, P.J. (1986), “Structural Analysis of Organizational Fields: A Blockmodel Approach,”Research in Organizational Behavior, 8, 335–370.Google Scholar
  42. Doreian, P. (1990). “Mapping Networks Through Time,” in: J. Weesie and H. Flap (Eds.),Social Networks Through Time, 245–264, ISOR/University of Utrecht, Belgium.Google Scholar
  43. Durfee E.H. (1988).Coordination of Distributed Problem Solvers. Boston, MA: Kluwer Academic Publishers.Google Scholar
  44. Durfee, E. H. and T.A. Montgomery (1991), “Coordination as Distributed Search in a Hierarchical Behavior Space,”IEEE Transactions on Systems, Man, and Cybernetics, 21(6), 1363–1378.Google Scholar
  45. Dutton, J.M. and W.H. Starbuck (1971).Computer Simulation of Human Behavior. New York: Wiley.Google Scholar
  46. Freeman, L.C. (1984), “Impact of Computer-based Communication on the Social Structure of an Emerging Scientific Specialty,”Social Networks, 6, 201–221.Google Scholar
  47. Galbraith, J.R. (1973).Designing Complex Organizations, Addison-Wesley Publishing Company.Google Scholar
  48. Galbraith, J.R. (1977).Organization Design, Addison-Wesley Publishing Company.Google Scholar
  49. Gasser, L, I. Hulthage, B. Leverich, J. Lieb, and A. Majchrzak (1993), “Organizations as Complex, Dynamic Design Problems,” in M. Filgueiras and L. Damas (Eds.),Progress in Artificial Intelligence in Lecture Notes in Artificial Intelligence 727, Springer Verlag.Google Scholar
  50. Gasser, L. and A. Majchrzak (1994), “ACTION Integrates Manufacturing Strategy, Design, and Planning,” in P. Kidd and W. Karwowski (Eds.)Ergonomics of Hybrid Automated Systems IV, IOS Press, Netherlands.Google Scholar
  51. Gasser, L. and A. Majchrzak (1992) “HITOP-A: Coordination, Infrastructure, and Enterprise Integration,”Proceedings of the First International Conference on Enterprise Integration, Hilton Head, South Carolina: MIT Press.Google Scholar
  52. Gasser, L. and I. Toru (1991). “A Dynamic Organizational Architecture for Adaptive Problem Solving,”Proceedings of the Ninth National Conference On Artificial Intelligence, Anaheim.Google Scholar
  53. Gasser L. and M.N. Huhns (Eds.) (1989).Distributed Artificial Intelligence. Vol. 2, Morgan Kaufmann.Google Scholar
  54. Gibson, F.P. and D.C. Plaut (1995), “A Connectionist Formulation of Learning in Dynamic Decision-Making Tasks,” inProceedings of the 17th Annual Conference of the Cognitive Science Society, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  55. Glance, N.S. and B.A. Huberman (1993), “The Outbreak of Cooperation,”Journal of Mathematical Sociology, 17(4), 281–302.Google Scholar
  56. Glance, N.S. and B.A. Huberman (1994a), “Dynamics of Social Dilemmas,”Scientific American.Google Scholar
  57. Glance, N.S. and B.A. Huberman (1994b), “Social Dilemmas and Fluid Organizations,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  58. Gloves, T. and J. Ledyard J. (1977), “Optimal Allocations of Public Goods: A Solution to the Free-Rider Problem,”Econometrica, 45, 738–809.Google Scholar
  59. Granovetter, M.S. (1973), “The Strength of Weak Ties.”American Journal of Sociology, 68, 1360–1380.Google Scholar
  60. Granovetter, M.S. (1974).Getting a Job: A Study of Contacts and Careers. Cambridge, MA: Harvard University Press.Google Scholar
  61. Grefenstette, J.J. (1991), “Strategy Acquisition with Genetic Algorithms,” in L. Davis (Ed.)Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold.Google Scholar
  62. Gode, D.K. and S. Sunder (1993), “Allocative Efficiency of Markets with Zero Intelligence Traders: Market as a Partial Substitute for Individual Rationality,”Journal of Political Economy, 101(1), 119–137.Google Scholar
  63. Gode, D.K. and S. Sunder (1994), “Human and Artificially Intelligent Traders in Computer Double Auctions,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  64. Hannan, M.T. and J. Freeman (1977), “The Population Ecology of Organizations”American Journal of Sociology, 82, 929–64.Google Scholar
  65. Humman, N.P. and T.J. Fararo (1995), “Actors and networks as objects,”Social Networks, 17, 1–26.Google Scholar
  66. Hage, J. (1965), “An Axiomatic Theory of Organizations,”Administrative Science Quarterly, 10, 289–320.Google Scholar
  67. Hanneman, R. (1988).Computer-Assisted Theory Building: Modeling Dynamic Social Systems, Beverly Hills, CA: Sage.Google Scholar
  68. Hanneman, R., R. Collins, G. Mordit (1992). “Long-Term Dynamics of State Legitimacy and Imperialist Capitalism: A Simulation of Neo-Weberian Theory,” Unpublished Paper, Riverside, CA.Google Scholar
  69. Harrison, J.R. and G.R. Carrol (1991), “Keeping the Faith: A Model of Cultural Transmission in Formal Organizations,”Administrative Science Quarterly, 36, 552–582.Google Scholar
  70. Holland, J.H. and J. Miller (1991), “Artificial Adaptive Agents in Economic Theory,”American Economic Review, Papers and Proceedings 81, 365–70.Google Scholar
  71. Huang, Z. and M. Masuch (1993), “ALX3, A Multi-Agent ALX Logic,” CCSOM Working Paper 93-102.Google Scholar
  72. Huber, G. (1990), “A Theory of The Effects of Advanced Information Technologies on Organizational Design, Intelligence and Decision Making,”Academy of Management Review, 15(1), 47–71.Google Scholar
  73. Jin, Y. and R.E. Levitt (1994), “i-AGENTS: Modeling Organization Problem Solving in Multiagent Teams,”International Journal of Intelligent Systems in Accounting, Finance and Management.Google Scholar
  74. Kaufer, D. and K. Carley (1993).Communication at a Distance: The Effect of Print on Socio-Cultural Organization and Change. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  75. Krackhardt, D. and R.N. Stern (1988), “Informal Networks and Organizational Crises: An Experimental, Simulation,”Social Psychology Quarterly, 51(2), 123–140.Google Scholar
  76. Krackhardt, D. (1994), “Graph Theoretical Dimensions of Informal Organizations,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  77. Kumar, A., P.S. Ow, and M.J. Prietula (1993), “Organizational Simulation and Information Systems Design: An Operations Level Example,”Management Science, 39(2), 218–239.Google Scholar
  78. Lant, T.L. (1994), “Computer Simulations of Organizations as Experimental Learning Systems: Implications for Organization Theory,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  79. Lant, T.L. and S.J. Mezias (1990), “Managing Discontinuous Change: A Simulation Study of Organizational Learning and Entrepreneurship,”Strategic Management Journal, 11, 147–179.Google Scholar
  80. Lant, T.L. and S.J. Mezias (1992), “An Organizational Learning Model of Convergence and Reorientation,”Organization Science, 3(1), 47–71.Google Scholar
  81. Lawrence, P.R. and J.W. Lorsch, (1969),Developing Organizations: Diagnosis and Action, Addison-Wesley Publishing Company, Inc., Reading, Massachusetts.Google Scholar
  82. Lawrence P.R. and J. Lorsch. (1967).Organization and Environment: Managing Differentiation and Integration. Boston, MA: Harvard University.Google Scholar
  83. Levinthal, D. and J.G. March (1981), “A Model of Adaptive Organizational Search,”Journal of Economic Behavior and Organization 2, 307–333.Google Scholar
  84. Levitt, R.E., G.P. Cohen, J.C. Kunz, C.I. Nass, T. Christiansen and Y. Jin (1994), “The ‘Virtual Design’ Team: Simulating How Organization Structure and Information Processing Tools affect Team Performance,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  85. Lin, Z. (1994), “A Theoretical Evaluation Of Measures Of Organizational Design: Interrelationship And Performance Predictability,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  86. Mackenzie, K.D. (1978).Organizational Structures, AHM Publishing Corporation, Arlington Heights, Illinois.Google Scholar
  87. Macy, M.W. (1990), “Learning Theory and the Logic of Critical Mass,”American Sociological Review, 55, 809–826.Google Scholar
  88. Macy, M.W. (1991), “Learning to Cooperate: Stochastic and Tacit Collusion in Social Exchange,”American Journal of Sociology, 97(3), 808–43.Google Scholar
  89. Majchrzak, A. and L. Finley (1995), “A Practical Theory and Tool for Specifying, Sociotechnical Requirements to Achieve Organizational Effectiveness,” in: J.J. Benders, J. De Haan, and D. Bennett (Eds.)Symbiotic Approaches: Work and Technology, London: Taylor and Francis.Google Scholar
  90. Majchrzak, A. and L. Gasser (1992a), “Towards a Conceptual Framework for Specifying Manufacturing Workgroups Congruent with Technological Change,”International Journal of Computer-Integrated Manufacturing, 5(2), 118–131.Google Scholar
  91. Majchrzak, A. and L. Gasser (1992b), “HITOP-A: A Tool to Facilitate Interdisciplinary Manufacturing Systems Design,”International Journal of Human Factors in Manufacturing, 2(3), 255–276.Google Scholar
  92. Majchrzak, A. and L. Gasser (1991), “On Using Artificial Intelligence to Integrate the Design of Organizational and Process Change in US Manufacturing,”Artificial Intelligence and Society, 5, 321–338.Google Scholar
  93. Malone, T.W. (1987), “Modeling Coordination in Organizations and Markets,”Management Science, 33(10), 1317–32.Google Scholar
  94. Marschak, J. (1955), “Elements for a Theory of Teams,”Management Science, 1, 127–137.Google Scholar
  95. March, J. and R. Weissinger-Baylon (Eds.). (1986)Ambiguity and Command: Organizational Perspectives on Military Decision Making. Boston, MA: Pitman.Google Scholar
  96. March, J. and H. Simon (1958).Organizations. New York, NY: Wiley.Google Scholar
  97. Masuch, M. and Z. Huang (1994), “A Logical Deconstruction of Organizational Action: Formalizing J. D. Thompson's Organizations in Action, in a Multi-Agent Action Logic,” CCSOM Working Paper 94-120.Google Scholar
  98. Masuch, M. (1990).Organization, Management and Expert Systems: Models of Automated Reasoning Berlin, New York: Walter de Gruyter.Google Scholar
  99. Masuch, M. and P. LaPotin (1989), “Beyond Garbage Cans: An AI Model, of Organizational Choice,”Administrative Science Quarterly, 34, 38–67.Google Scholar
  100. Mezias, S.J. and M.A. Glynn (forthcoming), “Using Computer Simulation to Understand the Management of Technology: Applications for Theory Development,”Technology Studies.Google Scholar
  101. Mintzberg, H., (1983).Structures in Five: Designing Effective Organizations, Prentice Hall Inc.Google Scholar
  102. Padgett, J.F. (1980), “Managing Garbage Can Hierarchies,”Administrative Science Quarterly, 25(4), 583–604.Google Scholar
  103. Panning, W.H., (1986), “Information Pooling and Group Decisions in Non-experimental Settings,” in F.M. Jablin, L.L. Putnam, K.H. Roberts and L.W. Porter (Eds.),Handbook of Organizational Communication: An Interdisciplinary Perspective, Sage, Beverly Hills, CA.Google Scholar
  104. Papageorgiou, C.P. (1992), “Cognitive Model of Decision Making: Chunking and the Radar Detection Task,”CMU-CS Bachelors Thesis, Pittsburgh, PA.Google Scholar
  105. Patrick, S. (1974). “Complex Model Variations: The Affects of Selected Parameter Changes to Simulation, Outcomes,” Paper presented at the ORSA/TIMS Workshop on Mathematical and Computational Organization Theory.Google Scholar
  106. Pete, A., K.R. Pattipati and D.L. Kleinman (1993), “Optimal Team and Individual Decision Rules in Uncertain Dichotomous Situations,”Public Choice, 75, 205–230.Google Scholar
  107. Pfeffer, J. (1978).Organizational Design, AHM Publishing Corporation, Arlington Heights, Illinois.Google Scholar
  108. Radner, R. (1987).Decentralization and Incentives, University of Minnesota Press.Google Scholar
  109. Reuter, Peuter, Mitchell, Benett, and Grimes (1994). “The Training Impact Decisions System (TIDES): A Decision-Aiding System for Personnel Utilization and Training in U.S. Air Force Occupational Specialties,” Paper presented at the ORSA/TIMS Workshop on Mathematical and Computational Organization Theory.Google Scholar
  110. Scott, J. (1991).Social Network Analysis: A Handbook, Newbury Park, CA: Sage.Google Scholar
  111. Scott, W.R. (1987).Organizations: Rational, Natural, and Open Systems, Prentice Hall, Inc., Englewood Cliffs, New Jersey.Google Scholar
  112. Shapley L. and B. Grofman (1984), “Optimizing Group Judgmental Accuracy in the Presence of Interdependencies,”Public Choice, 43, 329–343.Google Scholar
  113. Simon, H.A. (1973), “Applying Information Technology to Organizational Design,” Public Administrative Review, 33, 268–278.Google Scholar
  114. Snijders T. (1990), “Testing for Change in a Digraph at Two Time Points,”Social Networks, 12, 539–373.Google Scholar
  115. Staw, B.M., L.E. Sanderlands and J.E. Dutton (1981), “Threat-Rigidity Effects in Organization al Behavior: A Multilevel Analysis,”Administrative Science Quarterly, 26, 501–524.Google Scholar
  116. Thompson, J.D. (1962/67).Organizations in Action. McGraw-Hill.Google Scholar
  117. Tsuchiya, S. (1993) “Artificial Intelligence and Organizational Learning: How Can AI Contribute to Organizational Learning?” Working Paper.Google Scholar
  118. Verhagen, H. and M. Masuch (1994), “TASCCS: a Synthesis of Double-AISS, and Plural-SOAR,” in K.M. Carley and M.J. Prietula (Eds.)Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  119. Wasserman, S. (1980), “Analyzing Social Networks as a Stochastic Process,”Journal of the American Statistical Association, 75, 280–294.Google Scholar
  120. Weber, M. (1922), “Bureaucracy,” in H. Gerth and C.W. Mills (Eds.),Max Weber: Essays in Sociology. Oxford, England: Oxford University Press.Google Scholar
  121. Wasserman S. and K. Faust (1994).Social Network Analysis: Methods and Applications, Cambridge University Press, Cambridge.Google Scholar
  122. Woodward, J. (1965).Industrial Organization: Theory and Practice, Oxford University Press, London.Google Scholar
  123. Zhou, C., P.B. Luh, and D.L. Kleinman (1993). “Modeling Distributed Team Resource Allocation within a Geographical Environment,”Proceedings of 1993 American Control Conference, San Francisco, CA: June 1993; IEEE Service Center, Piscataway, NJ: Vol 3: 2735–2739.Google Scholar
  124. Zweben, M. and M.S. Fox (Eds.) (1994).Intelligent Scheduling, Morgan Kaufmann Pub.Co., San Mateo CA.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Kathleen M. Carley
    • 1
  1. 1.Department of Social and Decision SciencesCarnegie Mellon UniversityPittsburgh

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